Pharmaceutical organizations face increasing pressure to align their internal decision-making processes with externally imposed regulatory frameworks — ICH quality guidelines, FDA 21 CFR Part 11, EMA guidance on AI, and the revised ICH GCP E6(R3). The HPF-P Framework's Decision Readiness Level (DRL) provides a structured five-stage readiness ladder, yet its integration with formal compliance re...
Decision Readiness Index (DRI) is the core metric of the HPF-P framework — a scalar signal summarising the information completeness required before a pharmaceutical portfolio decision can be trusted. Yet a single DRI snapshot provides only a point-in-time view. This article investigates how continuous, real-time monitoring of DRI signals transforms static readiness scores into dynamic control l...
This article presents a systematic comparative benchmarking of the Heuristic Prediction Framework for Pharmaceuticals (HPF-P) against three established portfolio management approaches: Markowitz mean-variance optimisation, Black-Litterman allocation, and naive machine-l[REDACTED]g selectors. Drawing on validated benchmarks from the HPF-P stress-testing study and supplemented by newly collected ...
Intelligence measurement stands at a critical inflection point. The accelerating saturation of static benchmarks — with median time-to-saturation declining from five years in 2019 to under one year by 2025 — demands a fundamental rethinking of how we evaluate artificial intelligence. This article projects the evolution of AI evaluation paradigms over the next decade (2026-2035), analyzing three...
The transition from deterministic SaaS workloads to non-deterministic agentic AI systems has fundamentally disrupted enterprise software pricing. Traditional per-seat licensing assumed predictable, bounded resource consumption per user. Agentic AI violates this assumption: autonomous agents consume 5-30x more tokens than simple chatbots, exhibit unpredictable usage patterns, and chain multiple ...
The dominant paradigm for AI memory — fixed-size context windows processed through self-attention — faces fundamental scalability barriers as large language models are deployed in long-horizon agentic tasks requiring hundreds of interaction sessions. This article investigates the transition from fixed context windows to persistent memory architectures through three research questions addressing...
This companion reference consolidates every mathematical variable, notation, and formula used across the FLAI and GROMUS research articles published on Stabilarity Research Hub. Researchers, practitioners, and reviewers who work with both frameworks will find unified definitions here, eliminating the need to cross-reference multiple papers. All definitions are sourced directly from the primary ...
The rapid expansion of AI memory architectures — from KV-caches and retrieval-augmented generation to parametric weight storage — has proceeded largely without systematic reference to the biological memory systems that inspired them. This article investigates three research questions about the structural and functional parallels between biological memory systems (hippocampal-cortical consolidat...
The expansion of large language model context windows to 128K+ tokens has reopened a fundamental architectural question: should AI systems remember through retrieval from external stores or through attention over internally maintained representations? This article investigates three research questions about the comparative performance of retrieval-augmented memory (RAM) and pure attention memor...
Retrieval-Augmented Generation (RAG) has become the dominant paradigm for grounding large language models in external knowledge, yet its runtime retrieval overhead imposes latency and consistency penalties that limit production deployability. Cache-Augmented Generation (CAG) proposes an inversion of this paradigm: preload all relevant documents into the model's key-value (KV) cache before queri...